code: def learn_from_batch(self, batch):
      # batch contains a list of episodes to learn from
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      # get the values for the current states
      state_value_head_targets = self.networks[
          "main"
      ].online_network.predict(batch.states(network_keys))
  
      # the targets for the state value estimator
      if self.ap.algorithm.targets_horizon == "1-Step":
          # 1-Step Q learning
          q_st_plus_1 = self.networks["main"].target_network.predict(
              batch.next_states(network_keys)
          )
  
          for i in reversed(range(batch.size)):
              state_value_head_targets[i][
                  batch.actions()[i]
              ] = batch.rewards()[i] + (
                  1.0 - batch.game_overs()[i]
              ) * self.ap.algorithm.discount * np.max(
                  q_st_plus_1[i], 0
              )
  
      elif self.ap.algorithm.targets_horizon == "N-Step":
          # N-Step Q learning
          if batch.game_overs()[-1]:
              R = 0
          else:
              R = np.max(
                  self.networks["main"].target_network.predict(
                      last_sample(batch.next_states(network_keys))
                  )
              )
  
          for i in reversed(range(batch.size)):
              R = batch.rewards()[i] + self.ap.algorithm.discount * R
              state_value_head_targets[i][batch.actions()[i]] = R
  
      else:
          assert (
              True
          ), "The available values for targets_horizon are: 1-Step, N-Step"
  
      # add Q value samples for logging
      self.q_values.add_sample(state_value_head_targets)
  
      # train
      result = self.networks[
          "main"
      ].online_network.accumulate_gradients(
          batch.states(network_keys), [state_value_head_targets]
      )
  
      # logging
      total_loss, losses, unclipped_grads = result[:3]
      self.value_loss.add_sample(losses[0])
  
      return total_loss, losses, unclipped_grads
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters
  from rl_coach.core_types import EnvironmentSteps
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
  from rl_coach.utils import last_sample
label_desc: Use image node_id 3 for calling the NStepQAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads